Although relational databases provide an easily understood model of data and the relationships inherent therein, not all data fits compactly into the model. Data that does fit well typically has no implicit relationships between items (tuples) of data. For example, a relation describing employees might have a single key, the employee number, together with several hundred or even thousand characters of information describing the employee (for example, home address, phone number, email address). In this case there is little or no relation between the tuple of data items of one employee and those of another. Tuples of this sort frequently have a high information content in relation to the size of the identifying keys and their associated indexes. Conversely, data that does not fit well has regular, strong relationships between tuples. For example, data representing a log of atmospheric temperature in three dimensions might be recorded as tuples having one field for time, three fields for the spatial coordinates and one for the temperature; or video data might be stored with fields {frame number, row, column, pixel value}. In these examples, there are strong relations between the coordinates and, frequently, strong correlations between the measured values themselves. Using a relational database to store such data requires a large amount of identifying information (for example, spatial or raster coordinates) to be stored explicitly, thereby increasing the storage required.